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Session 1106

An ANN Model for the Influence of Siding Materials on Single-Family Home Values

Mohammed E. Haque, Ph.D., P.E., Amro Taibah, Ph.D.

Texas A&M University, Texas, USA/ King Abdulaziz University, Saudi Arabia

Abstract

The real estate valuation is a complex process considering the range of variables that are known to play a role in determining such a value. The esthetics and choice of building’s façade materials can greatly influence a homebuyer’s decision. This paper evaluated such buyer’s preferences of various siding materials by estimating the market-clearing prices determined by the buyers of single-family homes (SFHs). The study employs a comparative hedonic estimation and artificial neural network (ANN) model. The study relied on a large sample of SFHs that were sold during the period from 1997 to 2000 in College Station, Texas. The analysis was restricted to homes that are highly homogeneous in their structural attributes in order to eliminate the impact of such attributes on the home values. The main aim of this study was to develop an ANN model to determine the influence of four different siding materials on home values. Several different ANN models with different layers/slabs connections, weights, and activation functions were trained. The presented ANN Jordan-Elman Nets with logistic activation function was the best one among all other trials. It converged very rapidly to produce a very high predictive efficacy. The trained model was evaluated using the data that was not used during the training, which also indicated very good agreement between the actual and ANN predicted price. Additionally the study was aimed to compare the hedonic value estimation and ANN value prediction methods. The results indicate that stucco siding had the most significant impact on the property value. The estimated implicit values of different siding materials were a measure of the importance of such materials to the homebuyer and was resembled in the form of a paid premium. Moreover, in comparing the hedonic results and ANN results, this study found that both analytical methods support one another and have assigned similar weights to the various construction types that have been studied. In addition ANN showed to have a higher predictive accuracy level than did the hedonic estimation. The findings extend the body of literature concerned with real estate value analysis and have significant implications in the realm of fund allocation decision making for a real estate developer.

Introduction

Artificial intelligence (AI) applications have gained a broad interest in civil/construction/ architectural engineering problems. Its applications are very extensive and interdisciplinary. The graduate students civil/construction/architectural students should especially be encouraged to learn various applications of computing techniques including artificial neural network (ANN), genetic algorithm (GA), etc. This paper highlights various applications of AI. As an example of

Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright  2003, American Society for Engineering Education

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Taibah, A., & Haque, M. (2003, June), An Ann Model For The Influence Of Siding Materials On Single Family Home Values Paper presented at 2003 Annual Conference, Nashville, Tennessee. 10.18260/1-2--12549